Abstract: Improvements in image quality can be obtained by leveraging non-linear neural-network based feature recovery across multiple imaging modalities. Input imaging data is acquired, including first imaging data and second imaging data acquired using different imaging modalities. The first imaging data can have lower image quality and a larger FOV than the second imaging data. The first and second imaging data can be aligned and the aligned regions can be used to train a neural network to minimize the difference between the second imaging data and output data processed from the overlapping portion of the first imaging data. Once trained, the neural network can be used to generate improved image quality output from all of the first imaging data.

Abstract: Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent data—optionally collected at a higher throughput—to ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

Abstract: An image reconstruction method includes: determining singles rates of radiation detectors in a frame of imaging data detected by the radiation detectors; determining an energy correction factor (Nwgt) for each radiation detector based on an energy spectrum distribution of gamma rays incident on the radiation detector during acquisition of the frame of imaging data; determining a singles live time correction factor for each radiation detector from the singles rate and the energy correction factor; determining a system coincidence live time correction factor from the system singles rate; for each line of response (LOR) connecting pairs of radiation detectors, determining a live time correction factor for the LOR from the determined singles live time correction factors of the pair of radiation detectors connected by the LOR and the determined system coincidence live time correction factor; and reconstructing the frame of imaging data using the determined LOR live time correction factors.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform an imaging method (100). The method includes: receiving imaging data on a frame by frame basis for frames along an axial direction with neighboring frames overlapping along the axial direction wherein the frames include at least a volume (k) and a succeeding volume (k+1) at least partially overlapping the volume (k) along the axial direction; and generating an image of the volume (k) using an iterative image reconstruction process in which an iteration of the iterative image reconstruction process includes: computing a local penalty function for suppressing noise over the volume (k) including reducing the value of the local penalty function in an overlap region; generating an update image of the volume (k) using imaging data from the volume (k) and further using the local penalty function.

Abstract: Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent data—optionally collected at a higher throughput—to ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

Abstract: Imaging data (20) are acquired by a PET scanner (6) or other imaging device. Iterative image reconstruction of the imaging data is performed to generate a reconstructed image (22). The iterative image reconstruction includes performing an update step (24) that includes an edge preserving prior (28) having a spatially varying edge preservation threshold (30) whose value at each image voxel depends on a noise metric (32) in a local neighborhood of the image voxel. The noise metric may be computed as an aggregation of the intensities of neighborhood image voxels of the reconstructed image in the local neighborhood of the image voxel. The edge preserving prior may be a Relative Difference Prior (RDP). For further noise suppression, during the iterative image reconstruction image values of image features of the reconstructed image that have spatial extent smaller than a threshold (38) may be reduced.

Abstract: A device (10) for measuring respiration of a patient includes a positron emission tomography (PET) or single photon emission computed tomography (SPECT) imaging device (12). At least one electronic processor (16) is programmed to: extract a first respiration data signal (32) from emission imaging data of a patient acquired by the PET or SPECT imaging device; extract a second respiration data signal (36) from a photoplethysmograph (PPG) signal of the patient; and combine the first and second extracted respiration data signals to generate a respiration signal (40) indicative of respiration of the patient.

Abstract: A device (10) for performing an amyloid assessment includes a radiation detector assembly (12) including at least one radiation detector (14). At least one electronic processor (20) is programmed to: detect radiation counts over a data acquisition time interval using the radiation detector assembly; compute at least one current count metric from the detected radiation counts; store the at least one current count metric associated with a current test date in a non-transitory storage medium (26); and determine an amyloid metric based on a comparison of the at least one current count metric with a count metric stored in the non-transitory storage medium associated with an earlier test date.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: determining a weighting parameter (13) of an edge-preserving regularization or penalty of a regularized image reconstruction of an image acquisition device (12) for an imaging data set obtained by the image acquisition device; determining an edge sensitivity parameter (?) of the edge-preserving algorithm for the imaging data set obtained by the image acquisition device; and reconstructing the imaging data set obtained by the image acquisition device to generate a reconstructed image by applying the regularized image reconstruction including the edge-preserving regularization or penalty with the determined weighting and edge sensitivity parameters to the imaging data set obtained by the image acquisition device.

Abstract: An imaging method (100) includes: acquiring first training images of one or more imaging subjects using a first image acquisition device (12); acquiring second training images of the same one or more imaging subjects as the first training images using a second image acquisition device (14) of the same imaging modality as the first imaging device; and training a neural network (NN) (16) to transform the first training images into transformed first training images having a minimized value of a difference metric comparing the transformed first training images and the second training images.

Abstract: Emission imaging data are reconstructed to generate a low dose reconstructed image. Standardized uptake value (SUV) conversion (30) is applied to convert the low dose reconstructed image to a low dose SUV image. A neural network (46, 48) is applied to the low dose SUV image to generate an estimated full dose SUV image. Prior to applying the neural network the low dose reconstructed image or the low dose SUV image is filtered using a low pass filter (32). The neural network is trained on a set of training low dose SUV images and corresponding training full dose SUV images to transform the training low dose SUV images to match the corresponding training full dose SUV images, using a loss function having a mean square error loss component (34) and a loss component (36) that penalizes loss of image texture and/or a loss component (38) that promotes edge preservation.

Abstract: Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent data—optionally collected at a higher throughput—to ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform an image reconstruction method (100) to reconstruct list mode data acquired over a frame acquisition time using a plurality of radiation detectors (17) in which the events of the list mode data is timestamped.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (14) operatively connected to a display device (20) and including at least one electronic processor (16) to perform an image acquisition and reconstruction method (101). The method includes: retrieving a non-voxel-based reconstructed image comprising non-voxel image elements from a picture and archiving communication system (PACS) database (24) to the workstation; at the workstation, generating at least one voxel-based resampled image from the non-voxel-based reconstructed image; and displaying the at least one voxel-based reconstructed image on the display device.

Abstract: A non-transitory storage medium stores instructions readable and executable by an electronic processor (20) to perform a method (100) for estimating singles rates for detectors (16) of a detector array (14) of a positron emission tomography (PET) imaging device (12).

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: performing iterative image reconstruction of imaging data acquired using an image acquisition device (12); selecting an update image from a plurality of update images produced by the iterative image reconstruction; processing the selected update image to generate a hot spot artifact map; and suppressing hot spots identified by the generated hot spot artifact map in a reconstructed image output by the iterative image reconstruction.

Abstract: A non-transitory computer readable medium storing instructions readable and executable by an imaging workstation (14) including at least one electronic processor (16) to perform a dataset generation method (100) operating on emission imaging data acquired of a patient for one or more axial frames at a corresponding one or more bed positions, the method comprising: (a) identifying a frame of interest from the one or more axial frames; (b) generating simulated lesion data by simulating emission imaging data for the frame of interest of at least one simulated lesion placed in the frame of interest; (c) generating simulated frame emission imaging data by simulating emission imaging data for the frame of interest of the patient; (d) determining a normalization factor comprising a ratio of the value of a quantitative metric for the simulated patient data and the value of the quantitative metric for the emission imaging data acquired of the same patient for the frame of interest; and (e) generating a hybrid data set

Abstract: A non-transitory computer-readable medium stores instructions executable by a processor to perform an acquisition and reconstruction method for a first image acquisition device. The method includes determining a scheduled acquisition time based on an attenuation map derived from imaging data from a second image acquisition device and a sensitivity matrix of the first image acquisition device; acquiring emission imaging data using the first image acquisition device, where the acquiring is scheduled to be performed over the scheduled acquisition time; during an initial portion of the acquiring, measuring a count or count rate of the acquired emission imaging data; adjusting the scheduled acquisition time based on the measured count or count rate to generate an adjusted acquisition time while continuing the acquiring; stopping the acquiring at the adjusted acquisition time; and reconstructing the emission imaging data acquired over the adjusted acquisition time to generate one or more reconstructed images.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: generating, from received imaging data, a plurality of intermediate images reconstructed without scatter correction from data partitioned into different energy windows; generating a fraction of true counts and a fraction of scatter events in the generated intermediate images; generating a final reconstructed image from the intermediate images, the fraction of true counts in the intermediate images, and the fraction of scatter counts in the intermediate images; and at least one of controlling the non-transitory computer readable medium to store the final image and control a display device (24) to display the final image.

Abstract: A non-transitory storage medium storing instructions readable and executable by an imaging workstation (18) including at least one electronic processor (20) to perform an image reconstruction method (100). The method includes: receiving emission imaging data (22) from an image acquisition device (12) wherein the emission imaging data has been filtered using an acquisition energy passband (18); generating filtered imaging data by filtering the emission imaging data with a second energy passband (90) that is narrower than an acquisition energy passband; reconstructing the filtered imaging data to generate an intermediate image; estimating one or more scatter correction factors (SCFs) from the intermediate image; and reconstructing the emission imaging data corrected with the estimated SCFs to generate a reconstructed image.